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\begin{document}
	
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	\begin{poster}{
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				 \includegraphics[height=5.5em]{congress_money2.jpg}
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			% Title
			{\sc %Sans Serif
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				The Campaign Finance Advantage of \\Congressional Incumbents}
			% Authors
			{\sc %Sans Serif
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				\vspace{.5em} Devin McCarthy\hspace{3em}
				devin.mccarhty@duke.edu\hspace{3em}
				Duke University
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				\headerbox{Summary}{name=abstract,column=0,row=0}{
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					{} Fouirnaies and Hall (2014), FH, examine the effect of winning congressional elections on campaign contributions. They use a regression discontinuity design, assuming that candidates in close congressional elections are quasi-randomly assigned victory. We replicate two of their models:
					\newline
					\newline
					\textbf{Model A} shows that winning a congressional election (i.e., becoming an incumbent) produces a significant increase in the candidate's party's share of campaign contributions in the following electoral cycle.
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					\newline
					\textbf{Model B} shows that incumbency primarily increases contributions from a specific type of contributor: "access-oriented" investors who are interested in developing relationships with lawmakers.
					\newline
					\newline
					We replicate both of these models and test their validity after  imputing values for the missing data in FH's dataset.
				}
				
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				\headerbox{Regression Discontinuity}{name=data,column=0,below=abstract, above=bottom}{
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					{} When Democratic candidates win a congressional race, Democrats' share of campaign contributions in the following electoral cycle in that district jumps up considerably. 
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					FH test their model with a variety of bandwidths. In this replication we use a bandwidth of 5 percent away from a tied election. Within this bandwidth, FH use a simple OLS model to predict the effect of incumbency and donor type on campaign contributions.
					
					\begin{tabular}{@{}c@{ }c@{ }c@{ }c@{}@{ }@{ }c@{ }c@{ }c@{ }c@{ }}
						\includegraphics[height=.8\linewidth]{Split_Plot.png}
					\end{tabular}
					
				}
				
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				\headerbox{Missingness Map}{name=miss,column=1, span=1, row=0}{
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					{} 
					Roughly 1/3 of the observations in FH's initial dataset were missing, primarily in the dependent variables.
					
					\begin{tabular}{@{}c@{ }c@{ }c@{ }c@{}@{ }@{ }c@{ }c@{ }c@{ }c@{ }}
							\includegraphics[height=.873\linewidth]{MissMap3.png}
					\end{tabular} 
					
				}
				
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				\headerbox{Replication}{name=coefplot,column=1,span=1, below=miss}{
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					\centering
					\smaller{\textbf{}}\\
						\begin{tabular}{@{}c@{ }c@{ }c@{ }c@{}@{ }@{ }c@{ }c@{ }c@{ }c@{ }}
							\includegraphics[height=.4\linewidth]{CoefModel1.png}
							\includegraphics[height=.4\linewidth]{CoefModel2.png}
							
						\end{tabular}					
				}
			
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			\headerbox{Substantive Effects with Uncertainty }{name=results,column=2,span=2,row=0}{
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				We show the substantive effect of the treatments in the two models using 1000 simulations of two scenarios: one with the treatment(Democratic win/Democratic win interacted with investor contribution), and one without a Democratic win.
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				Dotted lines indicate the means of the two scenarios. Dark shading represents the 90\% confidence interval of stochastic uncertainty. Light shading represents the 95 \% CI. 
				\\ 
				\begin{tabular}{@{}c@{ }c@{ }c@{ }c@{}@{ }@{ }c@{ }c@{ }c@{ }c@{ }}
							\includegraphics[height=.3\linewidth]{SubstMod1.png}
							\includegraphics[height=.3\linewidth]{SubstMod2.png}					
				\end{tabular}
				
			}	
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				\headerbox{Cross-Validation of Estimates}{name=robustness,column=2,span=2, below=results}{  
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					10-fold cross-validation of the estimates in the imputed data on the two models.
										
						\begin{tabular}{@{}c@{ }c@{ }c@{ }c@{}@{ }@{ }c@{ }c@{ }c@{ }c@{ }}
							\includegraphics[height=.3\linewidth]{CrossValMod1.png}
							
							\includegraphics[height=.3\linewidth]{10FoldCross.png}
						\end{tabular}
				}
				
				
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				\headerbox{Data Imputation}{name=impute,column=1,span=1,below=coefplot, above=bottom}{
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					{}We created five imputed data sets using the variables in FH's models along with the district's state as a cross-sectional variable and the election year as a time series variable. The coefficient plots show the melded estimates of these five data sets. The cross-validation and substantive effects charts also use the imputed data.
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						\begin{tabular}{@{}c@{ }c@{ }c@{ }c@{}@{ }@{ }c@{ }c@{ }c@{ }c@{ }}
							\includegraphics[height=.4\linewidth]{ImputeModel1.png}
							\includegraphics[height=.4\linewidth]{ImputeModel2.png}
						\end{tabular}
					
				}
				
				
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				\headerbox{RMSE Cross-Validation}{name=crossvals,column=2,span=1,below=robustness, 
					above=bottom}{
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					Density plots of the root mean squared errors of a 100-fold cross-validation of the two models.
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					\begin{tabular}{@{}c@{ }c@{ }c@{ }c@{}@{ }@{ }c@{ }c@{ }c@{ }c@{ }}
						\includegraphics[height=.39\linewidth]{RMSEMod1.png}
						
						\includegraphics[height=.39\linewidth]{RMSEcrossval.png}
					\end{tabular}
				}
				
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					\headerbox{Conclusion}{name=conclusion,column=3,span=1,above=bottom,below=robustness}{
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						{}The replication largely confirms FH's findings. The imputed data increased the strength of the coefficient on the incumbency treatment variable in both models. Cross-validation further confirmed the validity of the coefficients.
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						Nonetheless, the significant degree of overlap in stochastic uncertainty demonstrated by the simulation suggests that we should remain cautious about the findings of this study.
					

			}
			\end{poster}%
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